Local Binary Pattern for Facial Gender Identification Using a Kivy-Based Application

Wira Pranata Sebayang; Tomy Satria Alasi; Irwan Jani Tarigan

doi:10.55537/cosie.v5i4.1858

Authors

Wira Pranata Sebayang STMIK Methodist Binjai
Tomy Satria Alasi STMIK Methodist Binjai
Irwan Jani Tarigan STMIK Methodist Binjai

DOI:

https://doi.org/10.55537/cosie.v5i4.1858

Keywords:

local binary pattern, Identifikasi Jenis Kelamin, citra wajah, kivy, pemrosesan gambar

Abstract

Automatic gender identification based on facial images still faces challenges due to spatial variations and the complexity of visual features, while deep learning approaches such as Convolutional Neural Networks require high computational resources that are unsuitable for resource-constrained devices. This study aims to design, implement, and evaluate the performance boundaries of the Local Binary Pattern method for facial image-based gender identification integrated into a Python application using the Kivy framework. The research methodology utilized facial images from the UTKFace dataset, undergoing preprocessing through grayscale conversion, micro-texture feature extraction via the Local Binary Pattern operator, and gender classification using a threshold-based decision rule, all of which were fully integrated into the Kivy user interface. The system was evaluated across three scenarios with varying numbers of test images (80, 160, and 320 images). Testing results revealed a peak accuracy of only 43%, with variations in lighting, facial pose, and expression identified as the primary factors limiting overall accuracy. It can be concluded that while the Local Binary Pattern method was successfully integrated technically within the Kivy framework, its simplified mean-value feature representation remains insufficient for reliable gender identification under uncontrolled visual conditions, highlighting the need for richer feature representations in future research.

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References

[1] P. Tripathi, N. Kumar, M. Rai, P. K. Shukla, and K. N. Verma, “Applications of machine learning in agriculture,” in Smart Village Infrastructure and Sustainable Rural Communities, IGI Global Scientific Publishing, 2023, pp. 99–118.

[2] I. H. Sarker, “Machine learning: Algorithms, real-world applications and research directions,” SN Comput. Sci., vol. 2, no. 3, p. 160, 2021.

[3] P. Salzabila, A. Hafiz, and A. Setiawan, “Pengembangan Sistem Evaluasi Otomatis Dalam E-Learning Menggunakan Machine Learning,” Pendas: Jurnal Ilmiah Pendidikan Dasar, vol. 10, no. 2, pp. 641–649, 2025.

[4] A. P. Purnama, S. Amir, F. Syamsuddin, and F. Fitriansal, “Integrasi Algoritma Kecerdasan Buatan dalam Optimalisasi Efektivitas Komunikasi Visual,” DEIKTIS: Jurnal Pendidikan Bahasa dan Sastra, vol. 5, no. 4, pp. 3036–3052, 2025.

[5] C. Cahyaningtyas, C. Gudiato, and M. Sari, “Deteksi Ekspresi Wajah Manusia Menggunakan Metode Convolutional Neural Network,” Jurnal Fasilkom, vol. 15, no. 1, pp. 138–145, 2025.

[6] M. Fansyuri, “Klasifikasi Jenis Kelamin Berdasarkan Citra Wajah Menggunakan Algoritma Neural Network,” Journal of Research and Publication Innovation, vol. 2, no. 4, pp. 3784–3795, 2024.

[7] M. A. Satriawan and W. Widhiarso, “Klasifikasi Pengenalan Wajah Untuk Mengetahui Jenis Kelamin Menggunakan Metode Convolutional Neural Network,” Jurnal Algoritme, vol. 4, no. 1, pp. 43–52, 2023.

[8] A. L. S. Guntoro, E. Julianto, and D. Budiyanto, “Pengenalan Ekspresi Wajah Menggunakan Convolutional Neural Network,” Jurnal Informatika Atma Jogja, vol. 3, no. 2, pp. 155–160, 2022.

[9] P. Musa, W. K. Anam, S. B. Musa, W. Aryunani, R. Senjaya, and P. Sularsih, “Pembelajaran Mendalam Pengklasifikasi Ekspresi Wajah Manusia dengan Model Arsitektur Xception pada Metode Convolutional Neural Network,” Rekayasa, vol. 16, no. 1, pp. 65–73, 2023.

[10] M. S. Negara, M. Irzan, A. D. Haqqi, and F. Bimantoro, “Implementasi Convolutional Neural Network pada Multi-label Classification Wajah Manusia Berdasarkan Usia, Gender, dan Ras,” DIELEKTRIKA, vol. 11, no. 2, 2024.

[11] A. Sopian, D. Setiadi, A. Suryatno, and R. Agustino, “Computer Vision: Deteksi Masker Wajah Prediksi Usia Jenis Kelamin dengan Teknik Deep Learning Menggunakan Algoritma Convolutional Neural Network (CNN),” Jurnal Teknologi Informatika dan Komputer, vol. 10, no. 2, pp. 720–733, 2024.

[12] C. A. Marcelio, M. A. Azzikra, D. P. Mufazzal, A. R. Illahi, S. Al Husain, and A. Abdiansah, “Aplikasi Analisis Wajah, Klasifikasi Gender dan Prediksi Usia Menggunakan Deep Learning pada Dataset Citra Wajah Manusia,” Jurnal Media Infotama, vol. 20, no. 1, pp. 378–383, 2024.

[13] Y. I. Fajar, “Penerapan Tensorflow Dalam Prediksi Jenis Kelamin Dengan Menggunakan Algoritma CNN,” JICode: Jurnal Informatika dan Komputer, vol. 2, no. 1, pp. 122–129, 2025.

[14] N. C. I. Natun, M. A. Santhia, and Y. R. Kaesmetan, “Identifikasi Pengenalan Wajah Berdasarkan Jenis Kelamin Menggunakan Metode Convolutional Neural Network (CNN),” Journal of Technology and Informatics (JoTI), vol. 6, no. 1, pp. 50–57, 2024.

[15] R. Aditia, M. S. Arrafiq, and F. Afandi, “Implementasi Opencv Face Recognition Pada Real-Time Deteksi Umur Dan Jenis Kelamin Menggunakan Python dengan Metode Klasifikasi,” Jurnal Garuda Pengabdian Kepada Masyarakat, vol. 1, no. 2, pp. 46–55, 2023.

[16] A. L. Arda, M. F. Said, E. Y. Puspaningrum, and S. Alam, “Klasifikasi Motif Batik Yogyakarta menggunakan Neural Network dengan Fitur Local Binary Pattern,” SemanTIK: Teknik Informasi, vol. 11, no. 2, 2025.

[17] V. Betcy Thanga Shoba and I. Shatheesh Sam, “Empirical mode decomposition and local binary pattern based feature extraction for face recognition,” The Imaging Science Journal, vol. 72, no. 6, pp. 791–807, 2024.

[18] U. L. Sowjanya and R. Krithiga, “Decoding student emotions: An advanced CNN approach for behavior analysis application using uniform local binary pattern,” IEEE Access, vol. 12, pp. 106273–106284, 2024.

[19] A. A. Suaib, I. I. Tritosmoro, and N. Ibrahim, “Identifikasi Covid-19 Berdasarkan Citra X-Ray Paru-Paru Menggunakan Metode Local Binary Pattern Dan Random Forest,” Jurnal Teknik Informasi dan Komputer (Tekinkom), vol. 5, no. 2, p. 419, 2022.

[20] K. M. Hosny, W. M. El-Hady, F. M. Samy, E. Vrochidou, and G. A. Papakostas, “Multi-class classification of plant leaf diseases using feature fusion of deep convolutional neural network and local binary pattern,” IEEE Access, vol. 11, pp. 62307–62317, 2023.

[21] N. T. A. Pinem, I. P. G. Budisanjaya, S. Sumiyati, and N. N. Sulastri, “Rancang Bangun Aplikasi Mobile Berbasis Kivy untuk Estimasi Biomassa Tanaman Microgreen: Design and Development of a Kivy Based Mobile Application for Microgreen Plant Biomass Estimation,” Jurnal BETA (Biosistem dan Teknik Pertanian), vol. 13, no. 2, pp. 317–324, 2025.

[22] Y. Aufar and I. S. Sitanggang, “Face recognition based on Siamese convolutional neural network using Kivy framework,” Indonesian Journal of Electrical Engineering and Computer Science, vol. 26, no. 2, pp. 764–772, 2022.

[23] F. Suryani, “Pemanfaatan Framework Kivy untuk Pengembangan Aplikasi Pendidikan Interaktif Berbasis Android,” in Prosiding Seminar Nasional Amikom Surakarta, 2024, pp. 562–573.